52 lines
2.1 KiB
Markdown
52 lines
2.1 KiB
Markdown
# Documentation
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- Class name: WAS_Image_Threshold
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- Category: WAS Suite/Image/Process
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- Output node: False
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- Repo Ref: https://github.com/WASasquatch/was-node-suite-comfyui
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WAS_Image_Threshold 节点旨在通过应用阈值来处理图像，这对于从灰度输入创建二进制图像非常有用。它在图像分割和特征提取任务中发挥关键作用，通过指定的阈值简化图像为其核心结构。
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# Input types
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## Required
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- image
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- 图像参数对于节点的操作至关重要，因为它是将被处理的输入。它通过确定将经历阈值处理的内容影响执行，这对于最终输出的清晰度和细节至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: PIL.Image.Image
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## Optional
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- threshold
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- 阈值参数非常重要，因为它决定了图像中像素值分类的截止点。它通过控制灰度值转换为黑色或白色（基于它们是否满足或超过阈值）直接影响节点的输出。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- thresholded_image
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- 阈值化图像输出代表了应用阈值后输入图像的处理结果。它很重要，因为它提供了输入的二进制版本，通常用于进一步分析或作为更复杂的图像处理工作流程中的一步。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class WAS_Image_Threshold:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {'required': {'image': ('IMAGE',), 'threshold': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.01})}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'image_threshold'
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CATEGORY = 'WAS Suite/Image/Process'
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def image_threshold(self, image, threshold=0.5):
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return (pil2tensor(self.apply_threshold(tensor2pil(image), threshold)),)
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def apply_threshold(self, input_image, threshold=0.5):
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grayscale_image = input_image.convert('L')
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threshold_value = int(threshold * 255)
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thresholded_image = grayscale_image.point(lambda x: 255 if x >= threshold_value else 0, mode='L')
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return thresholded_image
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``` |